Artificial Intelligence : Proceedings of AITA 2023, Volume 1

• Record Nr.: UNINA9910841868303321
• Autore: Sharma Harish
• Pubbl/distr/stampa: Singapore : , : Springer, , 2024; ©2024
• ISBN: 981-9984-76-9
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (495 pages)
• Collana: Lecture Notes in Networks and Systems Series ; ; v.843
• Altri autori (Persone): ChakravortyAntorweep; HussainShahid; KumariRajani
• Disciplina: 006.3
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Contents -- Editors and Contributors -- Control Techniques for Vision-Based Autonomous Vehicles for Agricultural Applications: A Meta-analytic Review -- 1 Introduction -- 2 State-of-The Art Studies -- 2.1 Target Detection in Autonomous Vehicle System -- 2.2 Vision-Based System -- 3 Mathematical Modeling of Autonomous System -- 4 Conclusion -- References -- Co-GA: A Bio-inspired Semi-supervised Framework for Fake News Detection on Scarcely Labeled Data -- 1 Introduction -- 2 Related Work -- 2.1 Supervised Fake News Detection Using Linguistic Content -- 2.2 Semi-supervised Fake News Detection Using Linguistic Content -- 2.3 Metaheuristics-Based Approaches for Feature Selection -- 2.4 Metaheuristics-Based Fake News Detection -- 3 Data -- 4 Proposed Methodology -- 4.1 Pre-processing -- 4.2 Feature Extraction -- 4.3 Bio-inspired Feature Selection -- 4.4 Multi-view Co-training Model -- 5 Results and Analysis -- 6 Future Research Directions -- 7 Conclusion -- References -- Kernel Methods for Conformal Prediction to Detect Botnets -- 1 Introduction -- 2 Related Works -- 2.1 Signature-Based and Heuristic-Based Botnet Detection -- 2.2 Machine Learning for Botnet Detection -- 2.3 Kernel Methods -- 2.4 Conformal Prediction -- 2.5 Deep Learning and Graph-Based Approaches -- 2.6 Challenges and Limitations -- 2.7 Motivation for the Proposed Approach -- 2.8 ; Emerging Trends and Research Directions -- 3 Methodology -- 3.1 Kernel Methods -- 3.2 Conformal Prediction -- 3.3 Proposed Approach: Kernel Methods for Conformal Prediction -- 3.4 Evaluation Metrics -- 3.5 Experimental Setup -- 4 Results -- 4.1 Dataset Description -- 4.2 Experimental Setup -- 4.3 Experimental Results -- 4.4 Analysis of Results -- 5 Conclusion -- References -- Biogas Generation from Animal Waste: A Case Study of Village Wazirpur -- 1 Introduction.; 2 Biogas Production from Animal Waste -- 2.1 Factors Affecting Biogas Production -- 2.2 Sensors for Determining the Parameters Affecting Biogas Production -- 3 Area Under Study -- 4 Cost Analysis and Electricity Production -- 5 Conclusion -- References -- Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory -- 1 Introduction -- 2 Problem Statement -- 3 Research Questions -- 4 KALSTM: A Hybrid Model -- 5 Results and Limitations -- 6 Conclusion -- References -- Modelling Stock Prices Prediction with Long Short-Term Memory (LSTM): A Black Box Approach -- 1 Introduction -- 2 Methodology Based on LSTM -- 3 Description of Datasets -- 4 Results and Discussions -- 5 Conclusion and Future Work -- References -- Agricultural Crop Yield Prediction for Indian Farmers Using Machine Learning -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Dataset -- 3.2 Methodology -- 4 Architecture -- 5 Result Analysis -- 6 Conclusion -- References -- Application of Artificial Intelligence on Camera-Based Human Pose Prediction for Yoga: A Methodological Study -- 1 Introduction -- 1.1 Scope -- 1.2 Challenges -- 1.3 Impact of Yoga [1] -- 2 Literature Review -- 3 Methodology -- 3.1 Research Process -- 3.2 Key Point Detection Methods -- 3.3 Implementation Methodology [12, 13] -- 4 Datasets and Metrics -- 5 Results -- 6 Conclusion -- 7 Future Potential Development -- References -- Predicting of Credit Risk Using Machine Learning Algorithms -- 1 Introduction -- 2 Review of Literature -- 2.1 Machine Learning Algorithms -- 2.2 Development of Credit Risk Model -- 3 Data and Methodology -- 3.1 Data -- 3.2 Variables -- 3.3 Machine Learning Models and Evaluation Parameters -- 3.4 Evaluation Parameters -- 3.5 Methodology -- 4 Empirical Findings -- 5 Conclusions and Implications -- References -- Study of Various Text Summarization Methods.; 1 Introduction -- 2 Literature Review -- 3 Overview of Proposed Model -- 3.1 Proposed Methodology -- 3.2 Design of Model Architecture -- 3.3 Model Evaluation -- 4 Results -- 5 Conclusion -- References -- Investigations on Deep Learning Pre-trained Model VGG-19 Using Transfer Learning for Remote Sensing Image Classification on Benchmark Datasets -- 1 Introduction -- 2 Literature Review -- 3 Comparison of Performance Metrics of Machine Learning Methods on the PatterNet Dataset -- 4 Utilizing Pre-trained Models for Transfer Learning -- 5 Transfer Learning with Pre-trained Models Based on the Baseline ImageNet Dataset -- 6 Overview of VGG-19 -- 7 Enabling Efficient Feature Reuse and Information Flow in Deep Neural Networks for Superior Performance -- 8 Deep Learning Surpassing Traditional Machine Learning Techniques -- 9 Setting Up Experiments: Feature Extraction and Classification for Remote Sensing Images with a Pre-trained VGG-19 Model -- 9.1 Dataset Description -- 9.2 Assessment Metrics Utilized for Model Evaluation in Image Classification and Retrieval -- 9.3 Research Findings: Investigating Test Accuracy and Test Loss Scores on Benchmark Datasets Using VGG-19 Pre-trained Model -- 10 Summarizing the Feature Extraction with Transfer Learning Approach in Deep Learning -- References -- Complexity Analysis of Legal Documents -- 1 Introduction -- 2 Related Works -- 2.1 NER for ; Indian Legal Documents -- 2.2 Information Extraction -- 2.3 Summarising in Legal Domain -- 2.4 Complexity of Legal Documents -- 3 Methodology -- 3.1 Proposed Model -- 3.2 Analysis of Complexity -- 4 Result Analysis -- 5 Conclusion and Future Works -- References -- Predicting Virality of Tweets Using ML Algorithms and Analyzing Key Determinants of Viral Tweets -- 1 Introduction -- 2 Theoretical Background and Related Work -- 3 Methodology -- 4 Results and Discussion.; 5 Conclusion, Limitations, and Future Scope -- References -- Review of Classification and Detection for Insects/Pests Using Machine Learning and Deep Learning Approach -- 1 Introduction -- 1.1 Pictorial Representation of Classification and Detection of Pests and Comparison Between ML and DL -- 2 Material -- 2.1 Dataset Collection -- 3 Literature Work -- 3.1 Review of Different Machine Learning and Deep Learning Techniques for the Classification of Pests -- 4 Conclusion -- References -- Sentiment Analysis of Product Reviews Using Deep Learning and Transformer Models: A Comparative Study -- 1 Introduction -- 2 Literature Review -- 3 Sentiment Analysis -- 3.1 Sentiment Analysis Based on Machine Learning -- 3.2 Sentiment Analysis Based on Deep Learning -- 3.3 Sentiment Analysis Based on Transformer-Based Models -- 4 Implementation -- 4.1 Dataset -- 4.2 Data Pre-processing -- 4.3 Classification Models -- 5 Results and Discussions -- 5.1 Hyper Parameters Used -- 5.2 Performance Evaluation -- 6 Conclusion -- References -- Effect of Variation in Pause Times Over MANET Routing Protocols -- 1 Introduction -- 2 MANET Routing Protocols and Literature Review -- 3 Environment Setup -- 4 Performance Metrics -- 5 Conclusions and Future Scope -- References -- DDCMR2: A Deep Detection and Classification Model with Resizing and Rescaling for Plant Disease -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology and Implementation -- 3.1 Data Collection -- 3.2 Data Cleaning, Preprocessing, and Visualization -- 3.3 Cache, Shuffle, and Prefetch -- 3.4 Model Building -- 3.5 Hyperparameters Choice -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- Leveraging Natural Language Queries for Effective Video Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology and Models -- 3.1 Uni-Modal Encoder -- 3.2 Cross-Modal Encoder.; 3.3 Query Generator -- 3.4 Query Decoder -- 4 Experimental Analysis and Outcomes -- 5 Conclusion -- References -- An Experimental Study to Perform Bioinformatics Based on Heart Disease Case Study Using Supervised Machine Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 Machine Learning -- 2.2 Logistic Regression -- 2.3 Decision Tree -- 2.4 Support Vector Machine -- 3 Experimentation -- 3.1 Data Provenance -- 3.2 Flow Diagram of This Study -- 3.3 Correlation Matrix -- 3.4 Logistic Regression -- 3.5 Support Vector Machine (SVM) -- 3.6 Decision Tree -- 4 Results and Analysis -- 5 Conclusion -- References -- Empirical Analysis of Denoising Algorithms for CCTV Face Images -- 1 Introduction -- 2 Related Work -- 3 BM3D (Block-Matching and 3D Filtering) -- 3.1 Collaborative Filtering: It Takes Four Steps -- 3.2 Aggregation -- 3.3 Wiener Filtering Step -- 4 KSVD (k-Singular Value Decomposition) -- 5 WNNM (Weighted Nuclear Norm Minimization) -- 6 Results and Discussion -- 7 Conclusion -- References -- Content-Based Tagging and Recommendation System for Tamil Songs Based on Text and Audio Input -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Methodology -- 3.1 Music Segmentation -- 3.2 Instrument Recognition -- 3.3 Lyric Collection and Translation -- 3.4 Lyric Tagging -- 3.5 Audio Prompt -- 3.6 Similarity-Based Retrieval -- 4 Datasets -- 4.1 MUSDB18 Dataset -- 4.2 Tamil Songs -- 4.3 AudioSet -- 5 Outcomes -- 5.1 Metrics for Evaluation -- ; 5.2 Summary of Metrics -- 6 Conclusions and Future Work -- References -- Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Preprocessing -- 3.2 Feature Extraction (BSIF) -- 3.3 Feature Level Fusion -- 3.4 Score Level Fusion -- 4 Experimental Results and Discussion -- 4.1 GTAV Dataset.; 4.2 FEI Face Database.